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6th IEEE International Conference on Artificial Intelligence and Knowledge Engineering, AIKE 2023

作者： Ho, Joshua Wang, Chien-Min King, Chung-Ta You, Yi-Hsin Feng, Chi-Wei Chen, Yen-Min Kuo, Bo-Yi Institute of Information Science Academia Sinica Taipei11529 Taiwan Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu30013 Taiwan Department of Computer Science National Tsing Hua University Hsinchu30013 Taiwan Department of Computer Science National Taiwan University Taipei10631 Taiwan Center of Intelligent Healthcare National Taiwan University Hospital Taipei10022 Taiwan

ISBN: (纸本)9798350331288

Over the last decades, there has been growing interest in research in multiple and interdisciplinary fields of human-AI computing. In particular, approaches integrating the intersecting design with reinforcement learning (RL) have received more attention. However, the current research on RL may need to consider its enhancement from a humaninspired approach further. In the present work, we focus on enabling a meta-reinforcement learning (meta-RL) agent to achieve adaptation and generalization according to modeling Markov decision processes using Bayesian knowledge and analysis. By introducing a novel framework called human-inspired meta-RL (HMRL), we incorporate the agent performing resilient actions to leverage the dynamic dense reward based on the knowledge and prediction of a Bayesian analysis. The proposed framework can make the agent learn generalization and prevent the agent from failing catastrophically. The experimental results show that our approach helps the agent reduce computational costs with learning adaptation. Finally, we conclude and anticipate that integrating human-inspired meta-RL can enable learning more formulations relating to robustness and scalability, leading to promising directions and more complex AI goals in the future. © 2023 IEEE.

关键词： Reinforcement learning

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Reimagining RViz: Multidimensional Augmented Reality Robot Signal Design

Reimagining RViz: Multidimensional Augmented Reality Robot S...

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IEEE International Workshop on Robot and human Communication (ROMAN)

作者： Thomas R Groechel Amy O’Connell Massimiliano Nigro Maja J Matarić Interaction Lab Department of Computer Science University of Southern California Los Angeles CA USA

ISBN: (数字)9781728188591

ISBN: (纸本)9781665406802

From RViz to augmented reality (AR), a wide variety of robot signal visualizations exist for conveying robot capabilities. Many of the visualizations designed for AR, however, have not isolated multiple salient Virtual Design Elements (VDEs) for a given signal and comparatively evaluated combinations of those VDEs. To address this, we identify multiple VDEs for AR signaling of the following core robot capabilities: navigation, light detection and ranging (LiDAR), camera, face detection, audio localization, and natural language processing. We evaluated each signal's VDE combinations with an Amazon Mechanical Turk study (n=150) where participants watched 4 videos for each signal (consisting of 2 independent VDE choices) and rated the clarity and visual appeal of each signal. The results define a set of the most clear and visually appealing signal visualization designs and inform about interaction effects among VDEs. The resulting VDEs offer design insights and a baseline for continued research into AR robot capability signalling.

关键词： Location awareness Visualization Laser radar Navigation Robot vision systems Natural language processing Robots

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Variable Selection for the Prediction of TSS, pH and TA of Intact Berries of Thompson Seedless Grapes from their NIS Reflection 30

Variable Selection for the Prediction of TSS, pH and TA of I...

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30th International Conference on Software, Telecommunications and computer Networks, SoftCOM 2022

作者： Chariskou, Chrysanthi Bazinas, Christos Daniels, Andries J. Opara, Umezuruike L. Nieuwoudt, Helene H. Kaburlasos, Vassilis G. Human-Machines Interaction Lab International Hellenic University Department of Computer Science Kavala Greece University of Stellenbosch Department of Viticulture and Oenology Stellenbosch South Africa University of Stellenbosch Faculty of AgriSciences Department of Horticultural Sciences Postharvest Technology Research Stellenbosch South Africa Institute for Wine Biotechnology University of Stellenbosch Department of Viticulture and Oenology Stellenbosch South Africa

ISBN: (数字)9789532901177

ISBN: (纸本)9789532901177

Wavenumbers of high absolute value of correlation coefficient to Total Soluble Solids (TSS), pH, or Titratable Acidity (TA) were selected from reflection Fourier transform near infrared (FT -NIR) spectra of intact grape berries of the white variety Thompson Seedless. Multiple linear regression (MLR) and partial least squares (PLS) regression were applied to the spectra to construct trained regression models able to predict TSS, pH, and TA. Square Pearson's correlation coefficient (R2) and the Mean Square Error (MSE) were used to evaluate the precision of prediction. TSS content was predicted with R2 score of 0.972 and MSE 0.094 using MLR and with R2 0.926 and MSE 0.223 using PLS regression. The pH prediction scores were R2 0.812 and MSE 0.002 with MLR. With PLS regression the values were R2 0.485 and MSE 0.004. TA can be predicted only from the second derivatives of the spectra. MLR produced R2 for prediction 0.745 and MSE 0.076, while the scores using PLS regression were R2 0.648 and MSE 0.114. It was concluded that variable selection could greatly improve the prediction accuracy. The appropriateness of the two regression methods depends on the structure of the spectra dataset and on the characteristics whose prediction is sought. © 2022 University of Split, FESB.

关键词： Multiple linear regression

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I was afraid, but now i enjoy being a streamer!

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Proceedings of the ACM on human-computer interaction 2021年第CSCW3期4卷 1–32页

作者： Chen, Xinyue Chen, Si Wang, Xu Huang, Yun Department of Information Management Peking University Beijing China School of Information Sciences University of Illinois at Urbana Champaign ChampaignIL United States Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA United States

The outbreak of COVID-19 has led to a sharp transition from offline to online education in many countries and areas. This transition heightens the intensity of existing challenges of online education, such as student attendance and education equality. During this time of uncertainty, the vast disparities in teachers? online experience and technical backgrounds, students' education level and their families' economic status, and schools' support, further pose new challenges to teachers and students. In this work, we study how Chinese teachers and students addressed challenges during this transition. We interviewed 15 teachers and 18 students from diverse backgrounds at varying education levels (K-12 and college). Our work makes timely and new contributions to the literature of online education. For example, our results showed that teachers applied Live Video Streaming (LVS) on multiple social media platforms and re-purposed different entertainment features to deliver online teaching for better student engagement;some teachers came to enjoy this new form of instruction after being resistant to it in the beginning, and students developed a better sense of intimacy with their teachers after experiencing certain online interactions. Our work also reveals the remaining challenges and prospects of LVS-based online education and sheds light on the future design of collaborative technologies for online education. © 2021 ACM.

关键词： Students

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Ubi-Care: An Elderly Life Support Healthcare Framework Based on Ubiquitous Personal Online Data Stores

Ubi-Care: An Elderly Life Support Healthcare Framework Based...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Chen, Hong Zhou, Tao Wu, Bo Graduate School of Human Sciences Waseda University Saitama359-1192 Japan Daiichi Institute of Technology Department of Information and Electronics System Engineering Taito Tokyo110-0005 Japan School of Computer Science Tokyo University of Technology Hachioji Tokyo192-0982 Japan

ISBN: (纸本)9781665410205

In recent years, the global aging population has intensified, leading to a sharp increase in social security benefits and caregiving costs. The elderly face greater health risks, and their behaviors often indicate signs of crises and illnesses. The rapid development of IoT technology offers new solutions for detecting abnormal signs, thereby promoting healthy aging, independent living, and social participation for the elderly. However, the diversity of IoT devices has led to the phenomenon of personal data silos. When the elderly leave their usual IoT environment, the issue of continuity in healthcare services becomes more pronounced. To address this issue, this study proposes a decentralized Ubi-Care framework. The framework aims to achieve complete separation of data and applications. It is based on the ActivityPub protocol and integrates various data from wearable devices, smart home sensors, and social networks, storing this data in a ubiquitous personal online data store (UPOD) and assigning different roles based on data type. UPOD applications support bidirectional following, allowing users to access data associated with relevant roles, addressing issues related to data categorization, sharing, privacy, and security. Additionally, this study proposes a method for utilizing complete UPOD data to perform anomaly detection based on the hidden Markov model (HMM). By effectively integrating IoT data into UPOD and enhancing data interoperability. The data-sharing model proposed in this study also facilitates elderly individuals and their family members in sharing relevant data as needed, while ensuring privacy protection. The methods proposed in this study will help prevent accidental injuries and enable early diagnosis of diseases, providing strong technical support for elderly healthcare. © 2024 IEEE.

关键词： Electronic health record

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Lost in Translation: A Cross-Cultural Examination of Linguistic Inaccessibility in HCI

Lost in Translation: A Cross-Cultural Examination of Linguis...

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2025 CHI Conference on human Factors in Computing Systems, CHI EA 2025

作者： Vigh, Eszter Weir, Ellen Tcherdakoff, Nathalie Alexandra Penglin Stangroome, Grace Jane Gu, Yelu Metatla, Oussama Watanabe, Mamoru Schäfer, René Hahn, Sophie Krawczyk, Konrad Mikolaj Godoy, Marcela Cossovich, Rodolfo Morin, Randy Dreaver-Charles, Kristine Koole, Marguerite Lewis, Frank B.W. Bristol Interaction Group University of Bristol Bristol United Kingdom Faculty of Engineering University of Bristol Bristol United Kingdom Bristol Interaction Group School of Computer Science University of Bristol Bristol United Kingdom University of Bristol Gloucestershire Bristol United Kingdom Department of Computer Science University of Bristol Bristol United Kingdom University of Bristol Bristol United Kingdom RWTH Aachen University Aachen Germany TU Delft Delft Netherlands NYU Shanghai Shanghai China Carleton Univeristy Ottawa ON Canada University of Saskatchewan Saskatoon SK Canada MADMUC University of Saskatchewan Saskatoon SK Canada

ISBN: (纸本)9798400713958

This paper examines linguistic and cultural diversity in human-computer interaction through multilingual experiences across various native languages, including Hungarian, Japanese, Cree, German, Welsh, Spanish, Mandarin, French, Polish, and Arabic. Each contribution reveals unique challenges in translation, usability, and cultural nuance within digital interfaces, with linguistic barriers ranging from issues with non-Latin characters to loss of contextual meaning and limited localisation options. These sections highlight the limitations of current design practices, which often prioritise English-centric frameworks that fail to accommodate diverse language structures and cultural nuances. By capturing these varied perspectives, this paper underscores the need for inclusive, cross-lingual design practices that address global usability challenges. It contributes to the development of more accessible and culturally sensitive digital environments, fostering an HCI approach that values linguistic diversity and cultural specificity. © 2025 Copyright held by the owner/author(s).

关键词： Accessibility Cross-cultural design Inclusivity Linguistic diversity User Experience

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SpeechMirror: A Multimodal Visual Analytics System for Personalized Reflection of Online Public Speaking Effectiveness

arXiv

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arXiv 2023年

作者： Huang, Zeyuan He, Qiang Maher, Kevin Deng, Xiaoming Lai, Yu-Kun Ma, Cuixia Qin, Sheng-Feng Liu, Yong-Jin Wang, Hongan The Beijing Key Laboratory of Human-Computer Interaction Institute of Software Chinese Academy of Sciences and also with the School of Computer Science and Technology University of Chinese Academy of Sciences China Diatom Design Limited Liability Company United States The School of Computer Science and Informatics Cardiff University United Kingdom The School of Design Northumbria University United Kingdom The Department of Computer Science and Technology MOE-Key Laboratory of Pervasive Computing Tsinghua University China

As communications are increasingly taking place virtually, the ability to present well online is becoming an indispensable skill. Online speakers are facing unique challenges in engaging with remote audiences. However, there has been a lack of evidence-based analytical systems for people to comprehensively evaluate online speeches and further discover possibilities for improvement. This paper introduces SpeechMirror, a visual analytics system facilitating reflection on a speech based on insights from a collection of online speeches. The system estimates the impact of different speech techniques on effectiveness and applies them to a speech to give users awareness of the performance of speech techniques. A similarity recommendation approach based on speech factors or script content supports guided exploration to expand knowledge of presentation evidence and accelerate the discovery of speech delivery possibilities. SpeechMirror provides intuitive visualizations and interactions for users to understand speech factors. Among them, SpeechTwin, a novel multimodal visual summary of speech, supports rapid understanding of critical speech factors and comparison of different speech samples, and SpeechPlayer augments the speech video by integrating visualization of the speaker's body language with interaction, for focused analysis. The system utilizes visualizations suited to the distinct nature of different speech factors for user comprehension. The proposed system and visualization techniques were evaluated with domain experts and amateurs, demonstrating usability for users with low visualization literacy and its efficacy in assisting users to develop insights for potential improvement. Copyright © 2023, The Authors. All rights reserved.

关键词： Modal analysis

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CL-MIL: Multi-Instance Learning on Whole-Slide Images with Self-Supervised Contrastive Learning

CL-MIL: Multi-Instance Learning on Whole-Slide Images with S...

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International Conference on computer and Communications (ICCC)

作者： Pengfei Xiu Dongying Liu Sheng Wang Hongliang Wang Wei Wang Shun Li Shenyang Institute of Computing Technology Chinese Academy of Science Shenyang China University of Chinese Academy of Sciences Beijing China Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China Department of Gastroenterology Shengjing Hospital of China Medical University Shenyang China School of Computer Science and Engineering Northeastern University Shenyang China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

Histopathological examination of tissue slides is considered the gold standard for clinical cancer diagnosis. However, it heavily relies on pathologists' subjective judgment and involves substantial workload. Existing convolutional neural network (CNN)-based whole slide image (WSI) diagnostic methods suffer from high annotation costs and complex data acquisition processes. Therefore, this study proposes a weakly supervised multiple instance learning method based on self-supervised contrastive learning (CL-MIL) for WSI classification. By integrating self-supervised contrastive learning strategies, leveraging data augmentation through H&E stain separation, and introducing causal intervention via backdoor adjustment to mitigate dataset bias, our approach significantly enhances feature representation and classification performance in pathological images. Experimental results on the Camelyon16 and TCGA lung cancer datasets demonstrate that our method outperforms other comparable WSI classification models across various evaluation metrics, achieving higher diagnostic efficiency and accuracy. Additionally, our method exhibits good interpretability, enabling precise localization of pathological regions and providing robust technical support for clinical applications.

关键词： Measurement Location awareness Pathology Accuracy Weak supervision Lung cancer Contrastive learning Feature extraction Standards Image classification

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An Intelligent Quantum Cyber-Security Framework for Healthcare Data Management

arXiv

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arXiv 2024年

作者： Gupta, Kishu Saxena, Deepika Rani, Pooja Kumar, Jitendra Makkar, Aaisha Singh, Ashutosh Kumar Lee, Chung-Nan Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung80424 Taiwan School of Computer Science and Engineering The University of Aizu Aizuwakamatsu965-0006 Japan Department of Computer Applications National Institute of Technology Kurukshetra Kurukshetra136119 India Department of Mathematics Bioinformatics and Computer Applications Maulana Azad National Institute of Technology Bhopal Bhopal462003 India College of Computer Science and Engineering University of Derby DerbyDE22 3AW United Kingdom Department of Computer Science and Engineering Indian Institute of Information Technology Bhopal Bhopal462003 India Department of Computer Science The University of Economics and Human Sciences Warsaw01-043 Poland

Digital healthcare is essential to facilitate consumers to access and disseminate their medical data easily for enhanced medical care services. However, the significant concern with digitalization across healthcare systems necessitates for a prompt, productive, and secure storage facility along with a vigorous communication strategy, to stimulate sensitive digital healthcare data sharing and proactive estimation of malicious entities. In this context, this paper introduces a comprehensive quantum-based framework to overwhelm the potential security and privacy issues for secure healthcare data management. It equips quantum encryption for the secured storage and dispersal of healthcare data over the shared cloud platform by employing quantum encryption. Also, the framework furnishes a quantum feed-forward neural network unit to examine the intention behind the data request before granting access, for proactive estimation of potential data breach. In this way, the proposed framework delivers overall healthcare data management by coupling the advanced and more competent quantum approach with machine learning to safeguard the data storage, access, and prediction of malicious entities in an automated manner. Thus, the proposed IQ-HDM leads to more cooperative and effective healthcare delivery and empowers individuals with adequate custody of their health data. The experimental evaluation and comparison of the proposed IQ-HDM framework with state-of-the-art methods outline a considerable improvement up to 67.6%, in tackling cyber threats related to healthcare data security. Note to Practitioner– This paper aims to address the issue of digital healthcare data access, which requires both ease and security. Existing research either focuses solely on safe access or on high security, which often comes with high computational challenges. In this paper, we present a comprehensive approach that takes into account various challenges such as secure data storage, efficient data commu

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Creating an Appropriate computer science and Computational Thinking Graduate Curriculum for K-12 Teachers: Context and Initial Results 12

Creating an Appropriate Computer Science and Computational T...

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12th IEEE Integrated STEM Education Conference, ISEC 2022

作者： Herbert-Berger, Katherine G. Hagiwara, Sumi Rogacki, Elizabeth M. Marlowe, Thomas J. Department of Computer Science Montclair State University Herbertk United States Department of Teaching and Learning Acting Associate Dean for Academic Affairs College of Education and Human Services Montclair State University Herbertk United States Department of Science Mount St Dominic Academy CaldwellNJ United States Department of Mathematics and Computer Science Seton Hall University United States

ISBN: (纸本)9781665484299

K-12 computer science education has challenges related to content and to teacher expertise and comfort. This is further made difficult with inconsistent standards and teacher preparation from state-to-state. We describe a K-12 computer science Teaching certificate program, located at Montclair State University, aimed at providing current teachers in northern New Jersey with enhanced understanding of computer science concepts, capabilities, and skills, plus scaffolding of equitable and inclusive teacher practices for applied CS pedagogy. We discuss a brief history of the field, our curriculum and approach and then our first graduating cohort's experiences and challenges. Finally, we discuss our future work. © 2022 IEEE.

关键词： Curricula

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